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DTSTART;TZID=Europe/Paris:20250325T160000
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DTSTAMP:20260502T021424
CREATED:20250603T090101Z
LAST-MODIFIED:20250603T090101Z
UID:7051-1742918400-1742918400@isdm.umontpellier.fr
SUMMARY:Combining T-learning and DR-learning: a framework for oracle-efficient estimation of causal contrasts
DESCRIPTION:Room Nadir\, Maison de la Télédétection (500 rue Jean François Breton)\nMachine Learning in Montpellier\, Theory & Practice – Lars Van der Laan \nWe introduce efficient plug-in (EP) learning\, a novel framework for the estimation of heterogeneous causal contrasts\, such as the conditional average treatment effect and conditional relative risk. The EP-learning framework enjoys the same oracle-efficiency as Neyman-orthogonal learning strategies\, such as DR-learning and R-learning\, while addressing some of their primary drawbacks\, including that (i) their practical applicability can be hindered by loss function non-convexity; and (ii) they may suffer from poor performance and instability due to inverse probability weighting and pseudo-outcomes that violate bounds. To avoid these drawbacks\, EP-learner constructs an efficient plug-in estimator of the population risk function for the causal contrast\, thereby inheriting the stability and robustness properties of plug-in estimation strategies like T-learning. Under reasonable conditions\, EP-learners based on empirical risk minimization are oracle-efficient\, exhibiting asymptotic equivalence to the minimizer of an oracle-efficient one-step debiased estimator of the population risk function. In simulation experiments\, we illustrate that EP-learners of the conditional average treatment effect and conditional relative risk outperform state-of-the-art competitors\, including T-learner\, R-learner\, and DR-learner. Open-source implementations of the proposed methods are available in our R package hte3. \n            Visio
URL:https://isdm.umontpellier.fr/event/combining-t-learning-and-dr-learning-a-framework-for-oracle-efficient-estimation-of-causal-contrasts/
CATEGORIES:Séminaire
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